WTO Working Paper ERSD-2014-19
Date: 24 October 2014
World Trade Organization
Economic Research and Statistics Division
Intellectual Property Rights Protection and Export
Diversification: the Application of Utility Model Laws
Kimm Gnangnon and Constance Besse Moser
Manuscript date: August 2014
Disclaimer: This is a working paper, and hence it represents research in progress. This paper
represents the personal opinions of individual staff members and is not meant to represent the
position or opinions of the WTO or its Members, nor the official position of any staff
members. The authors would like to express their warm thanks and gratitude to Kyoung Kim,
Keun Lee, Ginarte Park and Kineung Choo for having accepted to share with them the
database used in their study. Without this database, this study would not have been
performed. Thanks also to Annet Blank, Marc Bacchetta, Cosimo Beverelli, and Lanz Rainer
for their very helpful comments on an earlier version of this paper. Any errors or omissions
are the fault of the authors.
Division Development, World Trade Organization, rue de Lausanne 154, CH-1211 Geneva
21, Switzerland.
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Intellectual Property Rights protection and Export
Diversification: the application of Utility Model Laws
Kimm Gnangnon1 and Constance Besse Moser2
Abstract
We examine in this paper the impact of the tightening of IPRs, notably patents rights,
and the adoption of utility model laws on export diversification. To perform our analysis, we
used panel data covering 89 developing and developed countries (of which 55 developing
countries) over the period 1975 – 2003, and Lewbel (2012)'s instrumental variable technique.
Our results lead us to conclude that for developing countries, legal protection for minor and
adaptive inventions could be a springboard for further strengthening of IPRs protection in
spurring export diversification, which is essential for the structural change needed for their
economic development.
Keywords: Intellectual Property Rights; Utility Model Laws; Export Diversification;
Lewbel Instrumental Variable Technique.
Jel Classification: F14, O24, O34
1
2
Kimm Gnangnon is an Economic Affairs Officer in the Development Division, WTO.
Constance Besse Moser is a Consultant in the Development Division, WTO.
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1. Introduction
Over the past decades, the world economy has witnessed a steady rise of integration on
several dimensions including, through a greater mobility of factors of Production (capital and
labor), Trade, Foreign Direct Investments (FDI), and enforcement of Intellectual Property
Rights (IPRs). The latter has been the subject of abundant literature, notably on IPRs and
Trade, IPRs and FDI, IPRs and growth and IPR and Innovation. For example, with respect to
the literature on IPRs and growth, the importance of the differential effects of IPRs on
countries at different stages of their economic development has been explored (see for
example, Fink and Maskus, 2005). Furthermore, the issue as to whether the level of
intellectual property protection matters for the economic development level has been studied
(see Kim et al., 2012). With respect to IPRs and trade, the literature has examined the extent
to which the strength of IPRs could spur trade.
However, to our best knowledge, there is currently no study, which has investigated the
relationship between intellectual property protection and export diversification.
This paper contributes to the extant empirical literature on the effects of IPRs protection by
exploring whether or not the level of intellectual property protection matters for export
diversification. More specifically, we draw on many insights from the work of Kim et al.,
(2012) and consider the impact of not only the strength of IPRs protection, but also of the
different types of IPRs (namely, patents and utility model laws) on the countries' degree of
export diversification. A special emphasis has been put on the level of economic development
of these countries.
We obtain evidence that irrespective of the group considered (developed countries
versus developing countries), both strengthening of patents rights protection and adoption of
utility model laws are conducive to export diversification. However, while the impact of
tightening patents rights protection on export diversification is higher in developed countries
than in developing ones, the effect of utility model laws adoption affects similarly the two
groups of countries.
The remainder of the paper proceeds as follows. Section 2 provides an insight into
Intellectual Property; Section 3 briefly reviews the literature on the determinants of export
diversification; Section 4 discusses how different types of IPRs could affect the countries'
degree of export diversification, depending on the level of economic development of the
country; Section 5 presents the model specification and describes the data; Section 6
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discusses the estimation strategy; Section 7 presents the estimations' results; and Section 8
concludes.
2. Insight into Intellectual Property and its effects
2.1 Intellectual Property Rights: Patents and Utility Models
This sub-section briefly describes patents and utility models laws and explains their
differences. It draws among others, on Kim et al. (2012) and Smith (2014).
Intellectual Property (hereinafter, IP) refers to creations of the mind - including
inventions, literary artistic works, symbols, names, images and designs used in commerce which are featured to be non-rivalrous and non-excludable. However, Intellectual Property
Rights (hereinafter, IPRs) are laws regarding the protection of intellectual property, which
describe the ways in which the creators of IP can control its use. Hence, they are provided to
create a private market for what would otherwise be a public good with non-rivalrous and
non-excludable characteristics.
There are several forms of IPRs, of which the main are patents, copyrights, trademarks
and service-marks, plant breeders' rights, sui genesis1 rights, and trade secrets. Among all
these forms of IPRs, patents are the ones that protect inventions2. Utility models constitute an
alternative to patents and are adopted in some countries to protect inventions.
While there is a global acceptance of the term "patent", this is not the case for the
"utility models". This is because the latter differ fundamentally from one country to another.
"Utility model" is a generic term, which refers to subject matter that hinges precariously
between protectable under patent law and sui generis design law (Suthersanen, 2001). In the
intellectual property legislation, this term does not have a clear legal definition, while in the
WTO TRIPS Agreement, there is no specific reference to the utility model, it is recognized
under the Paris Convention. This lack of recognition at international level is exemplified by
the fact that the WTO TRIPS Agreement does not make a specific reference to utility model –
or second tier- protection, which may lead WTO3 Members to freely formulate or reject
second tier protection regimes. Nonetheless, TRIPS' Article 2.1 provides that the relevant
provisions of the Paris Convention (including Article 1.2) be extended to all WTO countries.
Whilst Utility models are recognized under the Paris Convention as industrial property,
the Convention does not provide any definition nor scope of utility models but simply
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confirms that the international principles of national treatment and the right of priority be
accorded to utility models. The Article 1.2 of the Paris Convention reads as follows: "The
protection of industrial property has as its object patents, utility models, industrial designs,
trademarks, service marks, trade names, indications of source or appellations of origin, and
the repression of unfair competition".
Despite their common function as exclusive rights granted for invention, utility models
and patents differ in several important ways: first, patents are granted for novel and nonobvious inventions that have industrial applicability, while utility models are second-tier
protection for minor inventions, such as devices, tools and implements, particularly in the
mechanical and electronic fields (Bently and Shermann, 2001 provide a legal discussion of
utility models). Moreover, processes and methods of production are typically excluded in the
case of utility models. Second, the inventions covered by utility models are technically less
complex and less expensive to apply for, than those of patents. Third, the requirements for
qualifying for a utility model are less stringent than those for a patent. Hence, in practice,
utility models are sought for marginal innovations which may not meet the patentability
criteria (Beneito, 2006). Fourth and last, the length of protection tends to be higher for
patents than for utility models: patents are generally granted for 20 years duration from the
date of application, while the duration of utility models is 6-10 years.
All in all, we can follow Kim et al. (2012) and conclude that utility models and patents
differ in the types of innovation they protect: patents protect innovations of relatively high
inventiveness and utility models protect those of relatively low inventiveness.
In the next sub-section, we briefly examine the costs and the benefits of strengthening
IP rights.
2.2 Brief discussion on the benefits versus the costs of a strong IPRs protection
scheme
Although the design and implementation of IP protection can be traced to the period4
before the TRIPS era, the Agreement on TRIPS – which entered into force as Annex 1.C of
the Marrakech Agreement establishing the WTO on January 1, 1995 - remains to date the
most significant multilateral arrangement on IPRs. It establishes minimum standards of
intellectual property protection, which many say are similar to those of many industrialized
countries, and covers all the prominent forms of protection (Copyright and Related Rights,
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Trademarks, Geographical Indications, Industrial Designs, Patents, Layout-Designs
(Topographies) of Integrated Circuits,
Protection of Undisclosed Information, and Control
of Anti-Competitive Practices in Contractual Licences) in a single agreement. In addition, it
incorporates provisions of previous arrangements, of which the major WIPO5 conventions.
For its implementation, the TRIPS Agreement allows a phase-in-process of different
lengths depending on the economic development level of countries (See Article 65 and 66).
In particular, it required industrialized countries to comply with its obligations within a year
after its entry into force, that is, January 1, 1996; developing countries and transition
economies were required to implement TRIPS within five years after its entry into force, or
by January 1, 2000. An extension period of five additional years was provided to those
developing countries that did not previously have intellectual property protection in the
coverage areas of the Agreement. Least developed countries (henceforth, LDCs), the category
of poorest countries in the world designated by the United Nations and considered as such by
the WTO, were required to comply with the TRIPs obligations within 11 years of the entry
into force of the Agreement, that is, by January 1, 2006. These countries have put forward a
number of arguments, - including their special requirements, their economic, financial and
administrative constraints, and the need for flexibility so as to create a viable technological
base - to secure two extension periods6 for the compliance of the Agreement's obligations: the
first one (obtained on 30 November 2005), was a seven-and-a-half year extension,
terminating on July 1, 2013 and the second one (recently obtained, 12 June 2013) was an
eight-and-a-half year extension, terminating on 1 July 2021. Moreover, Paragraph 2 of the
latter Decision7 of the Council for TRIPS states that LDCs are supposed to make full use of
the flexibilities provided by the TRIPS Agreement to address their needs, including, create a
sound and viable technological base and overcome their capacity constraints supported by,
among other steps, implementation of Article 66.2 by developed country Members. Article
66.2 reads as follows: Developed country Members shall provide incentives to enterprises
and institutions in their territories for the purpose of promoting and encouraging technology
transfer to least-developed country Members in order to enable them to create a sound and
viable technological base.
This leads to a wider question of whether developing countries and specifically LDCs
really benefit from strengthening their IPRs protection.
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The issue of benefits of Intellectual Property protection is controversial in the
international literature, which, some argue, reflects a tension between the interests of the
developed countries and those of developing countries. The rationale behind this goes as
follows a relatively small number of developed countries are holders of the resources
required to innovate and tend to be the sources of intellectual property rights in the
international market whilst developing countries tend to be the recipients of intellectual
property via international flows.
Smith (2014) summarizes the discussion of the benefits and the costs associated with
strong/weak IP systems in developed versus developing countries. This discussion which
illustrates the reluctance of developing countries to strengthen their IPRs systems it replicated
as follows.On the developed countries side, the net effects of adopting strong IPRs are not
clear-cut. Indeed, these effects depend on the relative dominance of the market expansion and
monopoly power effects. The monopoly power effects stem from the fact that stronger IPRs
provide incentives to source firms to restrict their transfer of IP to markets where IPRs are
relatively strong. This allows them to apply for protection in the foreign market and decrease
their exports in order to extract monopoly prices. This effect can particularly occur when the
recipient markets have few close substitutes, weak imitative abilities, and/or few domestic
competing firms. The market expansion effects refers to the situation where the source firms
have strong incentives to transfer their IP to markets where IPRs are relatively strong, merely
because they can apply for protection in the foreign market and reduce the risk of its creation
been copied. This can be the case when domestic firms in the recipient markets have the
ability to imitate the intellectual property. The reluctance of developing countries (who are
recipients of IP) to strengthen their intellectual property laws despite the strong pressure from
developed countries (who are sources of IP) is rooted on their view that the costs of strong IP
laws outweighs their benefits: the latter come from the fact that the adoption of relatively
strong IP protection by the recipient would facilitate technology transfer and provide
domestic incentives for innovation. In contrast, the recipient of IP could opt for relatively
weak intellectual property protection to allow for domestic imitation and prevent monopoly
behaviour in its market, and avoid transfer of monopoly rents to developed countries firms,
loss of competitiveness of developing countries firms, and reduction in technology transfers
from industrialized to developing countries.
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In this debate, the position of developed countries seems somehow clear: as the sources
of IP, they would prefer the recipients' countries to adopt strong IP protection, either to
reduce risk of imitation, or allow monopoly behaviour in the recipient countries' market.
The empirical literature has not been silent on this subject-matter. Helpman (1993,
pp.1274) while questioning who benefits from tight intellectual property rights in less
developed countries, suggests that if anyone benefits, it is not the latter. In the same vein,
Barton (2004, 320) argues that "the strengthening of patent systems throughout the world
appears likely to strengthen the position of incumbent multinationals and disfavour the
independent development of technology by indigenous firms in developing countries".
Meanwhile, several arguments have been developed to justify the need for developing
countries to increase their protection of IPRs. Diwan and Rodrik (1991) argue that developed
and developing countries generally have different technology needs and, without the
protection of IPRs by developing countries, developed countries would not develop
technologies largely needed by the developing world. According to Taylor (1993, 1994) and
Yang and Maskus (2001), developed country firms may react to the lack of IPRs protection
by developing countries by making their technologies more difficult to imitate, which can
result in less efficient research technology and less innovation. Chen and Puttitanum (2005)
note that even if greater protection of IPRs does not directly benefit the developing world,
given the fact that strengthening of IPRs is often a condition for a developing country’s
accession to the WTO, gains from international cooperation that tightens IPRs in developing
countries could still increase world welfare. Maskus et al. (2004) argue that the higher the
impact of strengthening IPRs protection would be in developing countries, the higher the
ability of these countries to absorb technology. He therefore recommends them to focus their
resources on improving their absorptive capacities through improved governance,
strengthened education programs, targeted technology inducements, and competition policies.
In this respect, WTO in collaboration with other multilateral institutions and bilateral donors
has been significantly contributing to the attainment of these objectives in these countries and
particularly in LDCs, through its technical assistance and capacity building activities.
In this on-going debate, the LDCs have also tried to defend their interests on
handicrafts and biodiversity of natural substances. As underlined by Henry (2004), as far as
intellectual property is concerned, the most important issue for these countries is related to
natural substances and biopiracy. In two important Declarations of LDCs Trade Ministers
meetings - the 5th Ministerial meeting held in Maseru, Lesotho, in 2005 and the 6th
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Ministerial meeting held in Dar-es-Salam, Tanzania, in 2009 – the Ministers did not make
any allusion to strengthening intellectual property rights protection as a goal they should
pursue, though several paragraphs in these Declarations were devoted to intellectual property
rights. The focus of these declarations was rather on traditional knowledge and benefit
sharing from exploitation of genetic resources and traditional knowledge from LDCs, on an
effective technology transfer from developed countries to LDCs to help them create a viable
technological base and on the need of financial and technical assistance to help LDCs
implement their TRIPS obligations (see also Gathii, 2012).
For these countries as well as for many other developing countries, utility model laws
are of high importance and can progressively help them acquire the necessary technological
capabilities to spur innovation and establish a viable technological base. This argument
would therefore be the basis of the remainder of our analysis.
Given the foregoing discussion, we consider in the next section the theoretical effects
of strong IPRs protection systems on the ability of countries - depending on their economic
level – to diversify their exports. More interestingly, we examine whether or not the utility
model laws adopted by certain countries, in particular developing countries also matter for
their shifting away from exporting mainly primary products.
3. Brief literature review on the determinants of export diversification
The idea of export diversification dates back to the 1960s where the conduct of trade
policy in developing countries was mainly guided by the development theory. In fact, during
these periods, there were two concurrent theories: the classical trade theory that argues for
trade specialization according to the comparative advantage and the development theory that
claimed the need of adopting diversification strategies out of traditional commodities sector
towards manufactured goods. This strategy has proven its success in Asian economies in the
early 1970s. Nonetheless, the theoretical underpinnings8 of the export diversification strategy
appeared towards the end of 1970s, in particular with the new trade and endogenous growth
theories developed by Krugman (1979) and expanded by Grossman and Helpman (1991) and
Krugman (1995). The new trade theory, in contrast with the traditional trade theories tries to
explain the importance of trade flows such as intra-industry trade by taking into account
factors such as externalities and scale economies, the demand and taste of consumers and the
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product cycle. This theory, firstly developed by Krugman (1979), is based on a monopolistic
competition hypothesis where each variety of goods is produced by identical firms within
each industry and exported to all markets, given that consumers prefer variety without limit.
Hence, in this model, decrease in trade costs will result in adjustments in incumbents' export
volume, depending on the sectoral elasticity of substitution. Moreover, Grossman and
Helpman (1991) have developed a quality-ladder model whereby innovation driven by factor
endowments, improves the quality of exported goods. In fact, their hypothesis of
diversification along quality ladder rests on the hypothesis that high-wage leading countries
successfully innovate due to their natural resources abundance and continually improve the
quality of varieties in order to replace goods imitated by the low-wage followers.
More recently Melitz (2003) had taken into account the high heterogeneity in
productivity and size of firms, both within and between industries, to provide a better
understanding of heterogeneous firms' trade. These explanations have important implications
on trade and development policies: for example, according to Melitz (2003), an increase in
export variety - one of the sources of export diversification - can increase productivity given
that exporters are more productive than non-exporters. In his model, technology is supposed
to be exogenous to trade costs.
Although the empirical literature on the determinants is increasing, it remains far less rich
than the literature on the determinants of exports performance. This is probably because of a
lack of unified and systematic theoretical framework to perform simple empirical
investigations. This empirical literature can be classified into three categories: panel data
analysis (for e.g., Parteka and Tambieri, 2008 and 2013; Dennis and Shepherd, 2011; Agosin
et al., 2012; Klinger and Lederman, 2011), region-specific analysis (see for e.g., Gutierrez de
Pinerez and Ferrantino, 1997; Cabral, 2010), and country-specific analysis (see for e.g., Lim,
2010). Among all these studies, several potential candidates have been identified to explain
export diversification, of which, the trade and financial liberalization, the domestic financial
reforms, the country size (in economic terms, i.e. the level of economic development, and in
geographic and/or population terms), the distance to main trading partners, the exchange rate
volatility and overvaluation, the factor endowments (human capital and natural resources)
and subsequently the importance of human capital in determining a country's ability to
innovate, adapt and implement technologies developed in advanced economies (Mayer,
1996).
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More specifically, the importance of technology has been emphasized in many of these
empirical studies: Klinger and Lederman (2011, p.69) provide support to Imbs and Wacziard
(2003)'s empirical findings of stage of diversification driven by technological development,
by noting that "export discoveries would be the results of technological convergence as
developing economies adopt existing technologies to produce and export goods that are new
to their economies but old for the advanced economies. As economies approach the global
technological frontier, innovation becomes cutting edge".
Although the importance of technological progress in countries' export diversification
process has been highlighted in many of these studies, to our best knowledge, there has been
no attempt of assessing how intellectual property could influence export diversification.
4. Discussion on IPRs protection effects on export diversification
Suppose the products exported by a given country are the outcomes of several factors,
including the "knowledge capital". The latter varies in sophistication and inventive steps and
could take two forms as inputs9: industrial knowledge, which is patentable (patentable
innovation) and knowledge stemming from minor inventive activity (utility model
innovations). In technologically lagging economies or firms, the nature of production is less
reliant on Research and Development (R&D) activities (which are undeveloped) but rather on
the abilities of countries to duplicate or creatively imitate from product designs of
technologically advanced economies (see also Kim, 1997).
Within this framework, it is likely that the level of technological development
determines the use of either knowledge capital inputs by a country. In the meantime, the
protection by governments of these knowledge capital inputs could spur technological
development (as noted in section 2) and subsequently countries' export diversification
process. The production – and therefore the exports - of developing countries is therefore
likely to depend on utility model innovations whereas that of developed economies would
more likely be dependent on patentable innovations. It is worth highlighting that in developed
countries, the adoption of utility model laws would not primarily be motivated by the
promotion of industrial development, but rather be based on some legal grounds10 – e.g.,
provision of recourse against unfair appropriation of effort, even for minor inventive efforts(see Kim et al., 2012 for more details). In this context, the impact of the intellectual property
rights protection – i.e., here, the protection of knowledge inputs - on export diversification
would likely be hinging on the level of technological base (including through transfer of
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technology), though this protection could also contribute to the development of this
technological base.
By making a distinction between countries with different levels of economic
development, the latter being proxied by the income level, we consider in this study two
groups11 of countries: the High Income Countries (HICs) and the Lower and Middle Income
Countries (LMICs). In line with our previous discussion, the production in the latter will be
highly hinging on utility model innovations while the production in the former will be rather
dependent on patentable innovations. Developing economies (henceforth, LMICs) notably
the poorest, which lack technological capacity and are featured by a weak IPR protection
regime, will not be able to spur innovation and develop the required technological base that
would enable them to diversify their export away from the primary products (manufacturing
and services production require high level of technological development). However, by
adopting utility model laws to encourage and protect minor inventions, these countries could
take advantage of the relatively low level of innovation by progressively developing their
technological capacities, further spurring innovation and as result encouraging the
diversification of their exports structure. In addition, in low-income countries, there may be
some learning effect from past utility model innovations that can enhance the ability to
conduct more innovative research and hence the production of R&D invested in developing
patentable innovations. This could be an important contributor to export diversification in
these countries. Overall, gradual innovation in these countries could allow for a progressively
establishment of a viable technological base which will develop as countries accumulate
technological learning and enhance technological capabilities so as to better produce
patentable inventions at later stages (see also Kim et al, 2012 for this argument).
In the meantime, we could also expect an absence of statistically significant effect of
tighter IPRs protection on export diversification in developing countries, if the latter lacks the
technological capacities/base, the industrial knowledge, and the skill level or absorptive
capacity to really take advantage of the strengthening of IPRs protection in order to diversify
their export structure away from primary products. As noted by Naghavi (2005), the
effectiveness of the IPRs protection system is dependent on how it impacts on innovation,
market structure and technology transfer. It is important to highlight here that this reasoning
rests on the hypothesis that technologically lagging economies could in the first years of IPRs
laws strengthening, i.e., formative period of strong IPRs laws adopt utility models in order to
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pave the way for further strengthening IPRs laws. As a result, we should also expect the
strengthening of IPRs law in these countries to facilitate export diversification.
Conversely, in more advanced economies, we can expect both the strength of IPRs
protection and even the adoption of utility model laws to encourage export diversification
through the high level of technological development and innovation.
Granted that the availability of both patents rights and utility model systems mainly
affect the efficiency of knowledge production (Kim et al., 2012) and consequently the
possible diversification of exports, we can expect that in advanced economies, both the
strength of IPRs protection, notably patents rights and utility model systems would encourage
the diversification of exports structure through the high level of technological development
and innovation. Nonetheless, as we noted earlier (see section 3), the primary motivation for
adopting protection for utility models
laws in these countries would not be industrial
development, but rather non-business and non-economic reasons.
In the next section, we present the empirical model underlying our assessment of how
the strengthening of IPRs protection systems and the adoption of utility model laws affect
export diversification. For the analysis, we consider the entire sample comprising both HICs
and LMICs but also the sub-samples of HICs and LMICs.
5. Model Specification and Data Description
5.1 The Model Specification
The model presented below draws mainly on Agosin et al. (2012) who provide an
empirical analysis of the exports diversification determinants for countries around the world.
We amended slightly this model by introducing the intellectual property variables (namely
patents rights and utility models law adoption) and by taking into account only the variables
that could affect simultaneously both export diversification and the level of intellectual
property protection.
For assessing whether or not the level of IPRs protection matters for countries' export
diversification, we refer to the extant empirical literature on both the determinants of export
diversification (see for e.g., Agosin et al., 2012; Parketa and Tamberi, 2013) and the
determinants of Intellectual Property protection (see for e.g., Kim et al., 2012; Chen and
Puttitanum, 2005) and consider the following model:
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EDIi,t   0  1 IPRi,t   2UMLi,t   3 INOVi,t   4TPi ,t   5 EDUi,t   6GDPci,t
  7 GDPcsqi,t  i  t   i ,t
(1)
where i denotes the index of the country, t represents the index of the period (here, year); EDI
is the Export Diversification Index, measuring the degree of export diversification of a given
country in in a given year; IPR represents the level of intellectual property rights; UML is a
dummy variable capturing the adoption of a utility model laws by a given country; it takes the
value 1 if utility model laws exist in a country and the value 0, otherwise; INOV represents a
measure of Innovation; GDPc and GDPcsq denote respectively the GDP per Capita and its
square, both expressed in constant 2005 prices; GDPc is a proxy of the level of economic
development; TP is a measure of Trade Policy, capturing the degree of trade openness; EDU
represents a measure of human capital;  i is a set of country-specific effects;  t is a set of
year dummies (to account for the general trends in the index of export diversification, as well
as common factors to all countries not explicitly included in the model and that affect this
index).  i,t is the error-term. The coefficients of the regressors are parameters to be estimated.
Our variables of interest are mainly IPR, UML. The other explanatory variables act as control
variables in model (1) because they could potentially affect simultaneously the degree of
export diversification and our variables of interest. As noted above, the choice of these
controls is largely determined by reference to the existing literature.
Note that in the model (1), we further include a dummy variable "LMICs", which takes
the value 1 if a country belongs to the category of LMICs and, 0, otherwise (that is if the
country belongs to the category of HICs). This dummy variable is interacted with our
variables of interest cited above to evaluate the differential impact of these variables in
developing countries versus developed countries.
Expected Effects of IPRs protection and the adoption of Utility model laws on export
Diversification
From the theoretical discussion made in Section 3, we can summarize the expected
effects of IPRs protection tightening and the adoption of utility model laws as follows: strong
IPRs and/or adoption of utility model laws could reduce export concentration in both
developed and developing countries and the coefficient obtained on the latter could be
expected to be strong, and even higher than the one obtained on the former. In our specific
case, given the way of interpretation of our dependent variable, we should be expecting a
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negative sign of the coefficient associated with the variable IPR and a negative sign of the
coefficient associated with the variable UML. As emphasized by Kim et al. (2012), the
coefficient estimate associated with the variable UML should be interpreted with caution as it
only captures the marginal additional effect of adopting a utility model system on the degree
of export diversification. A statistical significance of this coefficient should not mean that
minor inventions are not produced or have no impact in other non-utility model countries.
Expected Effects of Innovation on export diversification: we can expect, all things being
equal that a country with a high level of innovation and technological development would be
able to develop its manufacturing and/or services sectors and therefore increase its degree of
export diversification. A positive impact of innovation on export diversification is expected
here (a negative sign of the coefficient of the variable "INOV"). As the strengthening of IPRs
systems and/or utility model laws systems could affect export diversification mainly through
innovation (though also possibly through transfer of technology), it is possible that we obtain
a statistically insignificant effect of the variable "INOV" if our variables of interest (IPR and
UML) appear to be statistically significant.
Expected Effects of the control variables on export diversification
-GDP and GDPsq: Dixit and Norman (1980) and Helpman and Krugman (1985) argue
in the context of the new trade theory, that market size directly affects the degree of product
differentiation, so that bigger countries can produce wider range of products, making them
less specialized. Of course, at lower level of development where capital is scarce and
investments projects are almost limited, there are limited diversification opportunities.
More recently, Imbs and Wacziard (2003) have shown evidence that economic
development is associated with increased diversification of employment and production
across countries. Klinger and Lederman (2004, 2011) and Cadot et al., (2011) have also
obtained empirical evidence that export diversification across products appear to increase
with the level of development up to a certain point.
Parteka and Tamberi (2013, pp.810) underline that more diversified (i.e. less
specialised) structures of economic activity can run in parallel with higher levels of per capita
output, which is one reason why development goes hand-in-hand with a better diversification
climate. Parteka and Tamberi (2013) study the determinants of export diversification in a
sample of developed and developing countries and show evidence that as countries develop
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(economic development measured by GDP per capita), export specialization decreases
(export diversification increases). Moreover, they explore a quadratic formulation of the
relationship between the level of economic development and the degree of export
diversification (by introducing GDP per capita and its square in their model) and conclude on
the significance of the quadratic formulation: the economic development level of a country is
associated with less export specialization, i.e., more export diversification; however some
reversal of the trend is plausible, because beyond a certain threshold of economic
development, countries can become more specialized. We tested this idea in our study by
including both GDP per capita and its square in our model. The signs of the coefficients of
these two variables are a priori unknown.
- Trade Openness (TP): Melitz (2003) shows that in a monopolistic competition mode,
each firm produces a different variety of the exported good so that trade liberalization can
induce export diversification by raising the number of exporters in those sectors facing
improved export opportunities. Conversely, for economies highly dependent in primary
commodities for their exports, traditional explanations such as the factor-endowment
Heckscher-Ohlin model can be appropriate when examining the potential effect of trade
liberalization on export diversification (see also Agosin et al., 2012 for this argument). In
those countries, trade reforms can induce export specialization or concentration by increasing
the profitability of traditional (commodities) sectors. Krugman and Venables (1990), and
Dennis and Shepherd (2011) also argue that trade liberalization can act as market extension,
whilst Costas et al., (2008) note that the potential gains generated by trade may cause major
product diversification. In this study, we could therefore expect a positive effect of trade
policy reform on export diversification (meaning a negative sign of the coefficient associated
with the variable "TP").
- Human Capital (EDU): According to Aghion and Howitt (1998), human capital
characteristics, among other factors, can affect general conditions for product diversification.
Agosin et al., (2012) argue on the basis of Melitz (2003)'s model that human capital
accumulation can lead to export diversification if it allows countries to change their
specialization patterns from commodities to manufactured goods. As a result, we expect a
negative effect of human capital accumulation on export concentration (i.e., a negative sign
of the coefficient of the variable "EDU").
In the next sub-section, we describe the data used in the study.
16
5.2 Data description
The analysis is conducted on a panel dataset of 89 developing and developed countries
(see Appendix 1 for the list of countries) over the period 1975-2003. The choice of the latter
is dictated by data availability on the variables used in the model (1)12. The sub-sample of
developing and developed countries, respectively Low-and-Middle-Income Countries
(LMICs) comprising 55 countries and High Income Countries (HICs) comprising 34
countries, are listed in Appendices 2 and 3.
To capture medium term effects (a way also to smooth out business cycles effects on
the variables) and account for the fact that changes in IPR schemes take time, we used fiveyear averages of the dependent variable and all the control variables, except for the dummy
UML. Hence, for our period of study, we obtained 5 sub-periods of 5-year intervals (19751979; 1980-1984; 1985-1989; 1990-1994; 1995-1999) and 1 sub-period of 4-year interval
(2000-2003).
Data on the dependent variable, EDI are obtained from the recent IMF Database on
Diversification Toolkit13 (released in June 2014). This Index, representing the overall export
diversification is computed by the use of Theil indices following the definitions and methods
used in Cadot et al. (2011). It is interpreted as the (reverse) measure of export diversification
because higher values indicate lower diversification.
Data on IPR, UML and INOV are drawn from a dataset used by Kim et al., (2012)14 in
their study and provided by them with us.
IPR is an Index of patent rights, measuring the patent protection levels. It was
developed by Ginarte and Park (1997) and updated by Park (2008). This index is to some
extent relatively "subjective" because as stressed by Hudson and Minea (2013, pp.68), it is a
“constructed” not a "measured” variable, and is based on Ginarte and Park's approach to the
assessment of the strength of patent regimes. However, we use it in this study on the ground
that it is the best measure available and as such is used in the empirical literature dealing with
Intellectual Property Rights issues. This index is available every five years over the period
1960-2005 and covers more than 120 countries. Accordingly, in our study, we consider this
variable at the beginning of each of our sub-periods. The Ginarte-Park index of patent rights
provides a score that reflects a given country's overall level of patent rights and restrictions at
a given point in time. The underlying data are based on statutory case laws, which interpret
and apply the statutes (Kim et al., 2012). The strength of patent rights is a composite index,
based on five categories of patent laws: duration of protection; subject matter that is
17
patentable; membership in international treaties; enforcement mechanisms available; and the
degree to which limitations on patent holders are not imposed (such as compulsory licensing)
(Kim et al., 2012). Each of these categories (computed per country and per time-period) is
scored a value ranging from 0 to 1, and the outweighed sum of these five values constitutes
the overall value of the patent right index. As a result, the Ginarte-Park index ranges from 0
(no patent system) to 5 (strongest level of protection): higher numbers therefore indicate
stronger protection.
The data on UML are extracted from Greene (2010). This dummy variable is used here
because of the lack of a continuous variable capable to measure the strength of protection of
minor inventions.
Regarding the Innovation variable (INOV), the literature suggests two ways of
measuring: either through R&D expenditures, which aim at capturing the input innovation, or
through the number of patents applications and/or patents granted, which represent inventive
output. Because of the unavailability of R&D expenditures for many developing countries,
we follow the tradition in the empirical literature and use the number of patent applications
granted each year by the U.S. patent office to residents of a given country.
Data on the control variables stem from different sources. Data on per capita GDP are
sourced from the World Bank's World Development Indicators (WDI) 2014 and are
measured in 2005 US Dollars. We use Barro and Lee database to extract data on our measure
of human capital, EDU: it is defined here as the average years of total schooling for the total
population of a given country. Information on Trade Policy (TP) is measured by the Index of
the freedom to trade internationally whose information come from the Fraser Institute
(http://www.freetheworld.com). This Index ranges from 0 to 10, with a higher value
indicating a higher level of international trade freedom.
Summary of descriptive statistics and correlation coefficients among the different
variables over the full sample are reported respectively in Tables 1a and 1b (see below).
[Tables 1a to 1d, here]
[Table 2 here]
Table 2 (see above) provides some statistics on Utility models, IPRs, and Index of
export diversification, both on the full sample and the sub-samples, and over the period 20002003. More specifically, in the column of Utility models, we report the number of countries within each of the group of countries – in which there exists a system of utility model laws in
the year 2000 (because as mentioned above, this index is available only every five years); in
18
the column of IPR, the average values for each group of countries in the year 2000 are
displayed (this index is also only available every five years); in the last column of the table,
figures represent the average values of the variable EDI during the period 2000-2003. The
message conveyed by this table can be read as follows:
- During the period 2000 - 2003, and as expected, developed countries (HICs)
exhibited stronger IPR protection compared to those of developing economies (LMICs).
Furthermore, we note that, the higher the average strength of the patent right index, the lower
the average index of the export diversification, meaning that the developed countries with
higher IPR protection experience higher export diversification. However, this does not mean
at this stage of the analysis that the tightening of IPR protection (in 2000) causes export
diversification. Only the empirical analysis will demonstrate it.
- In the year 2000, out of 89 countries, only 29 had adopted a system of utility model
laws. It appears clearly and consistently with our prediction (see in particular section 4) that
HICs show a low propensity to adopt utility model laws (only 9 out of 34 countries have done
so) whereas LMICs show the reverse trend (20 out of 55 countries in our sample have
adopted this system).
6. Estimation Strategy
The estimation of our model (1) by the use of fixed effects will provide insignificant
results, merely because of little within variation in our main variables of interest, namely IPR
and UML as well as in our dependent variable, EDI. Moreover, the estimation of (1) by the
use of random effects will not sort out the problem of the little within-variation previously
mentioned because the random effects estimator is a matrix-weighted average of the betweenand within-effects, so whatever variation it picks up, is likely to be the between-variation in
our case (see also Chowdhury et al., 2014) for this argument. We therefore follow the
estimation strategy of the Chowdhury et al., (2014) which consists of estimating a pooled
panel data analysis where we control for common time effects. We also cluster the standard
errors at the country level.
The estimation of the pooled panel model by the use of ordinary least squares (OLS)
technique could generate inconsistent and biased coefficient because of potential endogeneity
that may arise from reverse causation and eventual measurement errors in our variables. For
example, we can expect countries with higher exports diversification to obtain a higher
19
number of US patents. Likewise, a double causality could be expected between each of the
variables (GDP, GDPsq, TP and EDU) and the dependent variable, EDI.
To handle this problem of endogeneity, the empirical econometric literature suggests
the use of instrumental variables. However, the latter are often unavailable or too weak
(Brown et al., 2014) and in our particular case, it is a tough task to find good instruments that
fit well with our model. To circumvent this difficulty, we adopt the alternative approach
(identification through heteroscedasticity) proposed by Lewbel (2012) that allows traditional
weak instrument testing and does not necessary require any external (outside) instrument.
Indeed, this approach simply consists in using any heteroscedasticity present in the data to
generate sets of instrument variables from within the model, even in the absence of any
suitable instrument. Hence, instead of identifying the endogenous variables in the secondstage equation based on traditional exclusion restrictions, Lewbel (2012) approach suggests
the identification by the use of higher moments. For the time being, this technique is valid
only for cross-sectional data and has been widely employed in the empirical research15.
To fix the ideas, let us consider a regression equation Y  X  W  e where X is a
vector of endogenous variables and W, a vector of exogenous variables. The approach
suggested by Lewbel (2012) is described as follows:
i) The researcher should identify a set of exogenous variable(s), denoted Z, where
Z  W, or even Z=W. External (outside) instruments could be included in Z.
ii) The endogenous variable(s) in X are regressed on the Z vector, followed by the
extraction of the residuals ê . The Lewbel (2012)'s technique is strongly hinges on the
hypothesis that the residuals ê must be heteroscedastic. Lewbel (2012) also suggests using
the standard Breusch - Pagan type test to detect heteroskedasticity in these residuals.
iii) If the latter condition is met, the residuals are consequently used to construct
the variable ( Z i  Z i ) * eˆi for the ith member of Z, where Z is the mean of Z i .
iv) This variable ( Z i  Z i ) * eˆi can be considered as the standard instrumental
variable in the second-stage regression. The latter can be estimated either by the standard IV
technique or by the use of the two-step feasible GMM (Generalized Methods of Moments).
In this study, the instruments are constructed by considering as endogenous all our
explanatory variables, except IPR and UML. In fact, we believe that the probability of bidirectional causality between IPR and EDI and, UML and EDI is very low. Moreover, even if
such causality exist, the fact that the variables IPR and UML are taken at the beginning of
20
each sub-period reduces severely the simultaneity bias. In addition, we include in the Z vector
an external instrument, which is the "terms of trade". We suppose this variable to be
correlated with the variables capturing GDP per capita and Trade Policy, while being
exogenous with respect to the variable EDI. The "terms of trade" is the ratio of export unit
value indexes to the import unit value indexes, measured relative to the base year 2005. Data
on terms of trade variable come from the Penn World Table version 8.0. The model based on
the pooled panel data is estimated in the second stage by the GMM technique16. For
comparison purposes, we also report ordinary least squares (OLS) estimates where standards
errors are also clustered at the country level.
7. Estimations Results
Table 3 and Table 4 (see below) present the results of the pooled panel model estimated
by employing respectively both OLS and Lewbel (2012)'s instrumental variable GMM
techniques. We first discuss the OLS estimations' results and them move to the results based
on Lewbel (2012)'s instrumental variable GMM technique. Note that because of the possible
collinearity between GDP, trade policy and IPR variables, we run the regressions with/and
without the GDP variables, namely per capita GDP and its square. This allows us to check
whether the magnitude and significance of our variables of interest are sensitive to the
exclusion of GDP variables.
[Tables 3 and 4, here]
Let us start the interpretation of our results with the estimations based on OLS
technique. Results of the model (1) estimations by OLS and without GDP variables are
displayed in columns [1], [2] and [3] of Table 3. We observe evidence of a significant
negative effect of IPR and UML variables on the dependent variable, suggesting that the
strengthening of IPRs protection and the adoption of utility model laws strongly encourage
export diversification. This observed impact applies to both LMICs and HICs, as the
coefficients associated with the interactions of these two variables with the variable LMICs
are statistically insignificant. In the meantime, an increase in innovation does not appear to
affect export diversification. As noted above, this is an expected result and may suggest that
the variables IPR and UML are capturing the effects of the variable "INOV" on export
diversification. Trade liberalization and accumulation of human capital are positively
associated with export diversification because the coefficients of these variables display the
expected sign and are significant at 5% level. Interestingly, innovation in LMICs appears to
21
better encourage export diversification than in HICs (the interaction between LMICs and
INOV variables is negative and significant at 1% level).
The introduction of GDP variables (see results in column [4] of Table 3) does not affect
the other estimates (when compared in particular with column [1] of the same table) but
seems to cancel out the effect of IPR on export diversification. This may be suggestive of
GDP variables absorbing the effect of IPR protection on the dependent variable. Nonetheless,
the effect of GDP variables on export diversification (non-linear relationship) is in line with
our predictions. That said, we also note that when we include interaction variables in the
model, the coefficient associated with the variable capturing the square of GDP per capita is
significant, but not the one associated with GDP per capita. In other words, an increase in the
per capita GDP leads to a concentration of exports. This surprising effect may likely hide a
possible differential effect depending on the sub-sample considered, but it may also attributed
to the possible endogeneity bias of the GDP variables.
As far as the variables IPR, UML, INOV, EDU and the interaction LMICs*INOV are
concerned, results in column [6] of Table 3 are roughly similar to those in column [3] of the
same table. Additionally, the strengthening of IPRs protection, although spurring export
diversification appears to exert a higher effect on HICs, compared to LMICs.
It is worth noticing that the degree of export diversification seems to be the same in
LMICs and HICs (see columns [2] and [4] of Table 3).
Let us now turn to the estimates based on the Lewbel (2012)'s GMM technique.
The results based on this econometric technique are reported in Table 4 (both with and
without the GDP variables).
[Table 4 here]
For the Lewbel GMM technique, the Breusch Pagan tests performed in the first-stages
regressions show a p-value = 0, suggesting the presence of heteroscedasticity in the residuals
extracted. In each of the columns [2], [4] and [6], the p-values associated with the
Kleinbergen Paap rk LM statistic shows that the null hypothesis of model under-identification
is rejected at the conventional level of 10% of statistical significance. In addition, in all these
columns, the null hypothesis that the identifying instruments are exogenous (Hansen test of
over-identification) is not rejected, as the p-values are always higher than 0.10 (for a
statistical significance at 10% level).
Let us focus now on our estimations' results reported in Table 4. Before proceeding to
the interpretation of these results, it is important to highlight that apart from the results
22
obtained on the variable "IPR", the OLS estimates of Table 3 are roughly similar to those
based on Lewbel GMM technique (in Table 4), though the level of statistical significance of
coefficients may sometimes differ.
More specifically, when examining columns [1], [2] and [3] of Table 4, we observe
evidence that the results are quite similar in terms of magnitude, although the statistical
significance of the coefficients are higher with the instrumental variable technique than with
OLS technique. Hence, strengthening of patents rights protection, adoption of utility model
laws, trade liberalization and improvement in human capital stock are conducive to export
diversification (all the coefficients – with the exception of the one associated with the
variable "EDU" are statistically significant at 1%). However, and once again in line with our
predictions, a rise in the degree of innovation appears to have no significant effect on export
diversification, although the expected sign is observed (except in column [1] of Table 4). The
results in columns [4], [5] and [6] of Table 4 are quite similar to those reported on the same
columns of Table [3], apart from few exceptions: one of our variable of interest, IPR, is
negatively and significantly (at 1% level of statistical significance) associated with the
variable EDI when we use Lewbel (2012)' GMM technique. Moreover, in contrast with OLS
estimates of GDP variables displayed in column [6] of Table 3, we obtain here evidence that
after a certain level of economic development, countries tend to increase their export products
specialization, thereby reducing the diversification of their exports. With respect to the
variable capturing innovation, we find that an additional patent granted by the US patent
office to a resident of LMICs will likely increase the degree of export diversification of these
countries compared to high income countries. Once again, we obtain a higher effect on export
diversification of strengthening of patents rights protection in developed countries compared
to developing countries.
Overall, our results lead us to conclude that not only IPR protection strength is
important for export diversification in both developed and developing countries, but also the
adoption of utility model laws by countries, irrespective of the level of economic
development, appears to be a strong determinant of export diversification. Granted, tightening
of patents rights protection has a higher effect on developed countries than on developing
ones, while adoption of utility model laws affects similarly these two groups of countries.
Additionally, countries that open up their economies to trade, and countries that improve their
human capital stocks are likely to better diversify their exports.
23
These results could dampen developing countries’ reluctance to strengthen their
intellectual property laws but also show their policymakers that the adoption of utility models
could be a springboard to diversify their exports, pending the progressive strengthening of the
intellectual property systems to establish a viable technological base.
8. Conclusion
This paper presents an assessment of the impact of strong Intellectual Property
Rights (IPRs) protection and adoption of Utility Model Laws on export diversification in both
developed and developing countries. The study covers 89 developing and developed
countries (of which 55 developing countries) over the period 1975 - 2003. Using Lewbel
(2012)'s Instrumental Variable approach in a pooled panel setting, we obtained evidence that
strong IPRs – notably patents rights - protection and adoption of utility models laws
encouraged significantly the diversification of exports in both developed and developing
countries. More specifically, the results suggest evidence that the adoption of utility model
laws exerts a strong positive effect on developing and developed countries alike. However,
the impact of strong IPRs protection in reducing the concentration of their exports baskets
appears to be higher in developed countries compared to developing ones.
In examining the economic growth effects of IPRs system, many authors (for e.g.,
Maskus, 2000; CIPR, 2002; Kumar, 2002; Dolfsma, 2006;) argued that developed and
developing countries that have achieved substantial growth rates have all fine-tuned their
IPRs system to match their development needs, rather than implement a wholesale IPRs
policy. In the same line, Kim et al., (2012) have also shown evidence that it is not only the
strength of intellectual property rights protection that matters for innovation and growth, but
also the type of protection. In other words, these authors have underlined that the availability
of legal protection for minor, adaptive inventions should most be useful to firms with low
technological capacities and limited resources. Developing countries, when acceding to the
WTO often agree to reform their IPRs systems as part of their accession negotiations. As
noticed by Adams (2008), the challenge of reforming IPRs systems requires a strategy of
maximizing the gains of tightening IPRs protection, while limiting the potentially adverse
effects of improved protection to facilitate the access of local entrepreneurs to the fruit of the
IPR systems, as it was the case in India and the Republic of Korea. In this respect, the
adoption of utility model laws by these countries, in particular the lower income countries
would be very helpful in creating incentives for innovation, technological development and
24
thereby export diversification. Such strategy, by acting as a springboard to export
diversification, could allow a progressive strengthening of the intellectual property systems in
order to establish a viable technological base. At the international level, lower income
countries could have the support of both the World Trade Organization (WTO) - given their
flexibilities in the TRIPs Agreement and despite the lack of an explicit reference to utility
model laws in this Agreement - and the World Intellectual Property Organization (WIPO)
conventions, one of which explicitly recognizes the importance of utility models for them.
Moreover, WTO and WIPO provide significant technical assistance to these countries.
Finally, it is worth highlighting that an important limitation in this study is our incapacity to
perform the analysis on the category of Least Developed Countries (LDCs) because of data
constraints. This could be a future research avenue when data becomes available.
25
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31
APPENDICES AND TABLES
Appendix 1: Full Sample considered in the dataset
Algeria
Czech Republic
Argentina
Denmark
Australia
Ecuador
Egypt, Arab
Austria
Bangladesh
El Salvador
Belgium
Finland
Bolivia
France
Brazil
Gabon
Bulgaria
Germany
Canada
Ghana
Chile
Greece
China
Guatemala
Colombia
Guyana
Congo, Dem Rep of
Haiti
Congo, Rep.
Honduras
Hong Kong,
Costa Rica
Hungary
Cyprus
Iceland
India
Indonesia
Iran, Islamic
Ireland
Israel
Italy
Japan
Jordan
Kenya
Korea, Rep.
Lithuania
Malawi
Malaysia
Mali
Malta
Mexico
Morocco
Singapore
Uruguay
Netherlands Slovak Republic Venezuela
New
South Africa
Zambia
Nicaragua
Spain
Zimbabwe
Niger
Sri Lanka
Norway
Sweden
Pakistan
Switzerland
Syrian Arab
Panama
Paraguay
Tanzania
Peru
Thailand
Trinidad and
Philippines
Poland
Tunisia
Portugal
Turkey
Romania
Uganda
Russian
Ukraine
Senegal
United Kingdom
Sierra
United States
Appendix 2: List of countries in the Sub-sample of Low and Middle Income Countries
(LMICs)
Algeria
Argentina
Bangladesh
Bolivia
Brazil
Bulgaria
China
Costa Rica
Hungary
Ecuador
India
Egypt, Arab Rep.
Indonesia
El Salvador Iran, Islamic Rep.
Gabon
Jordan
Ghana
Kenya
Guatemala
Malawi
Colombia
Guyana
Malaysia
Congo, Dem Rep of the
Congo, Rep.
Haiti
Honduras
Mali
Malta
Mexico
Senegal
Uganda
Morocco Sierra Leone Ukraine
Nicaragua South Africa Venezuela
Niger
Sri Lanka
Zambia
Pakistan Syrian Arab Zimbabwe
Panama
Tanzania
Paraguay
Thailand
Trinidad and
Peru
Tobago
Philippines
Tunisia
Romania
Turkey
Appendix 3: List of countries in the Sub-sample of High Income Countries (HICs)
Australia Czech Republic Hong Kong, China Korea, Rep.
Portugal
Switzerland
Austria
Denmark
Iceland
Lithuania
Russian Federation United Kingdom
Belgium
Finland
Ireland
Netherlands
Singapore
United States
Canada
France
Israel
New Zealand Slovak Republic
Uruguay
Chile
Germany
Italy
Norway
Spain
Cyprus
Greece
Japan
Poland
Sweden
32
Table 1a: Summary of descriptive statistics on the full sample as well as the sub-samples
Table 1a: Descriptive statistics on the Full Sample
Variable
EDI
IPR
INOV
GDPc
TP
EDU
Obs
518
506
459
511
498
534
Mean
2.99
2.41
2269.55
9876.43
5.89
6.63
Std. Dev.
1.17
1.07
12961.85
12343.61
2.46
2.83
Min
1.12
0.59
1.00
121.28
0.00
0.45
Max
6.04
4.88
178873.00
61753.46
9.97
12.69
Min
1.47
0.59
1.00
121.28
0.00
0.45
Max
6.04
4.42
791.00
14626.29
9.11
11.20
Min
1.12
1.33
1.00
2800.87
1.92
3.80
Max
4.33
4.88
178873.00
61753.46
9.97
12.69
Table 1b: Descriptive statistics on the sub-sample of LMICs
Variable
EDI
IPR
INOV
GDPc
TP
EDU
Obs
327
318
264
319
312
330
Mean
3.52
1.90
28.71
2316.31
4.71
5.13
Std. Dev.
1.07
0.79
80.58
2370.35
2.13
2.30
Table 1c: Descriptive statistics on the sub-sample of HICs
Variable
EDI
IPR
INOV
GDPc
TP
EDU
Obs
191
188
195
192
186
204
Mean
2.07
3.26
5303.30
22437.26
7.86
9.05
Std. Dev.
0.67
0.93
19507.64
11975.97
1.53
1.71
Table 1d: Bivariate Correlations: Full sample
Variables
EDI
IPR
UML
INOV
GDPc
TP
EDU
EDI
IPR
UML
INOV
GDPc
TP
EDU
1
-0.4867*
-0.1566*
-0.1770*
-0.3375*
-0.5156*
-0.5907*
1
0.2803*
0.3027*
0.6874*
0.6196*
0.6463*
1
0.0054
0.0377
0.2821*
0.1160*
1
0.2684*
0.1767*
0.2530*
1
0.6043*
0.5406*
1
0.6262*
1
Note: * indicates statistical significance at the 10% level.
33
Table 2: IPR, Utility Model and Export Diversification per group of countries, over the
period 2000-2003
Group of Countries
Full Sample
LMICs
HICs
Utility Model
Adoption
29
20
9
IPR
EDI
3.357
2.868
4.147
2.905
3.336
2.188
Note: The figures on IPR in this table represent the mean of each of these variables for each group of countries,
in the year 2000. By contrast, the figures related to the variable "Utility Adoption Model" indicate the number of
countries within each of the Group of countries that have adopted utility model laws in the year 2000. The
figures on EDI represent the average values of the variable EDI during the period 2000-2003.
LMICs without a Utility Model Law in 2000s: Bangladesh, Democratic Republic of the Congo, Guyana,
India, Iran, Islamic Rep., Jordan, Malawi, Malta, Nicaragua, Pakistan, Paraguay, Sierra Leone, South Africa, Sri
Lanka, Tanzania, Trinidad and Tobago, Tunisia, Uganda, Zambia, Zimbabwe.
HICs without a Utility Model Law in 2000s: Cyprus, Iceland, Israel, New Zealand, Norway, Singapore,
Sweden, United Kingdom, United States.
LDCs without a Utility Model Law in 2000s: Bangladesh, Democratic Republic of the Congo, Malawi, Sierra
Leone, Tanzania, Uganda, Zambia.
34
Table 3: Estimation's Results: OLS technique
VARIABLES
(1)
OLS
IPR
-0.256***
(0.0950)
-0.417**
(0.160)
-6.02e-07
(2.03e-06)
-0.106***
(0.0382)
-0.115***
(0.0391)
UML
INOV
TP
EDU
Dependent Variable: Export Diversification Index, EDI
(2)
(3)
(4)
(5)
OLS
OLS
OLS
OLS
-0.198*
(0.104)
-0.393**
(0.161)
-1.10e-06
(2.16e-06)
-0.0843**
(0.041)
-0.0916**
(0.0439)
-0.266**
(0.118)
-0.419**
(0.168)
-8.09e-08
(2.21e-06)
-0.083**
(0.038)
-0.090**
(0.0397)
0.341
(0.252)
-0.699***
(0.218)
-0.293***
(0.099)
-0.190***
(0.0671)
-0.153***
(0.0445)
-0.0610***
(0.0175)
5.237***
(0.541)
89
425
0.459
GDPc
GDPcsq
-1.009***
(0.163)
-0.442***
(0.0771)
-0.279***
(0.0496)
-0.206***
(0.0347)
-0.0793***
(0.0147)
5.662***
(0.383)
-0.791***
(0.234)
-0.346***
(0.107)
-0.220***
(0.0670)
-0.171***
(0.0441)
-0.0675***
(0.0167)
4.888***
(0.697)
-0.0269
(0.464)
0.207
(0.130)
0.0318
(0.272)
-0.004***
(0.0009)
-0.825***
(0.207)
-0.356***
(0.0922)
-0.228***
(0.0578)
-0.175***
(0.0380)
-0.076***
(0.015)
5.117***
(0.713)
89
431
0.435
89
431
0.444
89
431
0.503
LMICs
LMICs*IPR
LMICs*UML
LMICs*INOV
year1
year2
year3
year4
year5
Constant
Number of Countries
Observations
R-squared
-0.178
(0.137)
-0.385**
(0.162)
-1.58e-06
(2.20e-06)
-0.088**
(0.043)
-0.089**
(0.044)
-5.19e-05**
(2.09e-05)
1.12e-09***
(3.24e-10)
(6)
OLS
-0.173
(0.137)
-0.361**
(0.171)
-1.92e-06
(2.22e-06)
-0.082*
(0.044)
-0.082*
(0.046)
-4.08e-05
(2.80e-05)
9.68e-10**
(4.27e-10)
-0.330*
(0.177)
-0.316**
(0.156)
-1.64e-07
(2.24e-06)
-0.086**
(0.040)
-0.091**
(0.043)
-2.51e-05
(2.81e-05)
7.89e-10*
(4.03e-10)
0.224
(0.300)
-0.635***
(0.227)
-0.268**
(0.103)
-0.175**
(0.0682)
-0.145***
(0.0452)
-0.058***
(0.017)
4.892***
(0.680)
-0.312
(0.459)
0.334*
(0.178)
-0.088
(0.260)
-0.0042***
(0.0009)
-0.708***
(0.196)
-0.293***
(0.0860)
-0.191***
(0.0569)
-0.153***
(0.037)
-0.067***
(0.016)
5.296***
(0.698)
89
425
0.461
89
425
0.523
Note: *p-value<0.1; **p-value<0.05; ***p-value<0.01. Robust Standard Errors are in parenthesis. The EDI variable as
constructed by the IMF is in fact a concentration Index. Hence, an increase in EDI means an increasing concentration of
exports and a decrease in EDI signifies export diversification. OLS represents the Ordinary Least Squares technique where
standard errors are clustered at the country level.
35
Table 4: Estimation's Results: Lewbel (2012)' GMM technique
VARIABLES
IPR
UML
INOV
TP
EDU
(1)
Dependent Variable: Export Diversification Index, EDI
(2)
(3)
(4)
(5)
Lewbel
Lewbel
Lewbel
Lewbel
Lewbel
-0.255***
(0.062)
-0.484***
(0.096)
2.23e-06
(1.76e-06)
-0.108***
(0.035)
-0.0895***
(0.0319)
-0.211***
(0.064)
-0.472***
(0.090)
-7.13e-07
(1.61e-06)
-0.126***
(0.039)
-0.0583*
(0.0354)
-0.216***
(0.058)
-0.421***
(0.111)
-1.35e-06
(1.25e-06)
-0.113***
(0.034)
-0.062**
(0.031)
-0.218***
(0.08)
-0.43**
(0.08)
-7.43e-07
(1.45e-06)
-0.096***
(0.034)
-0.064**
(0.028)
-0.00004***
(0.000015)
8.75e-10***
(2.19e-10)
-0.242***
(0.073)
-0.394***
(0.088)
-1.49e-06
(1.47e-06)
-0.121***
(0.0315)
-0.035
(0.027)
-0.00002
(0.00002)
6.53e-10**
(2.32e-10)
-0.238***
(0.074)
-0.356***
(0.091)
-1.48e-06
(1.16e-06)
-0.127***
(0.024)
-0.056**
(0.025)
-0.000033**
(0.000014)
8.38e-10***
(1.98e-10)
0.255
(0.163)
0.285*
(0.163)
-0.809***
(0.156)
-0.350***
(0.073)
-0.243***
(0.049)
-0.176***
(0.034)
-0.064***
(0.015)
5.199***
(0.309)
0.0001
0.365
-0.745***
(0.140)
-0.339***
(0.066)
-0.240***
(0.044)
-0.177***
(0.031)
-0.064***
(0.014)
4.90***
(0.337)
0.027
0.338
-0.084
(0.213)
0.186**
(0.0897)
-0.022
(0.145)
-0.004***
(0.0006)
-0.677***
(0.119)
-0.303***
(0.056)
-0.210***
(0.038)
-0.157***
(0.026)
-0.062***
(0.013)
5.142***
(0.315)
0.027
0.595
89
416
0.449
89
416
0.445
89
416
0.506
GDPc
GDPcsq
Underidentication test
Hansen Over-ID test
-1.047***
(0.1459)
-0.4519***
(0.0680)
-0.2979***
(0.0426)
-0.2018***
(0.0297)
-0.075***
(0.015)
5.467***
(0.248)
0.0000
0.305
-0.891***
(0.175)
-0.400***
(0.084)
-0.275***
(0.051)
-0.198***
(0.033)
-0.072***
(0.015)
5.045***
(0.499)
0.0003
0.051
0.197
(0.273)
0.115
(0.087)
-0.010
(0.168)
-0.0045***
(0.0007)
-0.786***
(0.145)
-0.343***
(0.067)
-0.235***
(0.042)
-0.170***
(0.027)
-0.069***
(0.013)
4.90***
(0.46)
0.038
0.204
Number of Countries
Observations
R-squared
89
418
0.423
89
418
0.428
89
418
0.489
LMICs
LMICs*IPR
LMICs*UML
LMICs*INOV
year1
year2
year3
year4
year5
Constant
(6)
Lewbel
Note: *p-value<0.1; **p-value<0.05; ***p-value<0.01. Robust Standard Errors are in parenthesis. The EDI variable as
constructed by the IMF is in fact a concentration Index. Hence, an increase in EDI means an increasing concentration of
exports and a decrease in EDI signifies export diversification. Under-identification test refers to Kleinbergen-Paak rk LM
statistic and its p-value. Hansen Over-ID test is the Hansen test of over-identification. OLS represents the Ordinary Least
Sqaures technique where standard errors are clustered at the country level. Lewbel GMM is the estimation method based on
identification-through heteroscedasticity a la Lewbel (2012), performed here by the use of the two-step feasible GMM
(Generalized Methods of Moment).
36